ITI41225 Complex Systems Modelling and Digital Evolution (Autumn 2026)

One of the challenges in our digitalized society is to model and predict the behavior of the complex systems that surround us. Complex systems are systems made of a large set of components interacting locally and giving rise to an emergent behavior without centralized control. Complex systems are all around us, for example social networks, the neurons in the brain, an artificial neural network, the stock market, the weather, a smart city, a biological ecosystem, virus spread, and many more.

This course will introduce different complex systems models, such as cellular automata, networks, and agent-based models, and how to program and use them to model real world system. A particular focus will be given to artificial intelligence applications. Methods for programming complex systems models will include an introduction to biologically inspired and sub-symbolic AI methods, such as digital evolutionary algorithms, neuro-evolution, and evolutionary robotics.

Knowledge

The student

• has a solid understanding of complex systems theory and modelling, including cellular automata, network, and agent-based models
• has knowledge on how to use optimization methods to program complex systems to produce a wanted behavior, in particular using biologically inspired methods such as digital evolution, neuro-evolution, and evolutionary robotics
• becomes familiar with the benefits and drawbacks of these methods, parameter control, convergence, diversity, elitism, etc.
• has a clear understanding of key concepts in complex systems such as emergence, self-organization, adaptation, evolution.
• can relate underlying concepts and general principles from different complex systems

Skills

The student is able to

• model and analyse complex systems using cellular automata, networks and agent-based models
• program complex systems and optimize them using biologically inspired tools
• design and implement evolutionary methods such as evolutionary algorithms and neuro-evolution, and decide on necessary representations and evolutionary operators

General competence

The student:

• has theoretical and practical understanding of complex systems modelling
• can understand and discuss relevance, strength and limitations of complex systems models and biologically inspired optimization methods such as evolutionary algorithms
• is able to work in relevant research projects and implement different evolutionary algorithms by using a package and/or coding his/her own algorithms

Project (individual or in groups of 2-3 students) and individual oral exam.

The exam is divided into two parts:

• 50% of the grade is based on the project. All students in the group will share the same grade.
• The individual oral exam (50%) is based on the course curriculum. Duration approx. 20-30 min. No supporting materials are allowed.

Grading scale A - F in both parts. Both parts of the exam must be passed to pass the course. The student will get an individual joint grade for the entire course.

Resit / rescheduled exam is carried out at the same time as the next ordinary examination. The department management can nevertheless decide to arrange a resit exam in the following semester.

Upon re-examination, each part of the examination can be retaken. Upon re-examination, a new project must be carried out.

This course is evaluated by a:

• Final course evaluation

The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee at the Department of Computer Science and Communication.